Hypertension and outcomes research From clinical trials to clinical epidemiology

Outcomes research seeks to identify effective evidence-based methods of providing the best medical care. While randomized clinical trials (RCT) usually provide the clearest answers, they are often not done or not practicable. More than a decade after the introduction of calcium channel blockers and angiotensin converting enzyme (ACE) inhibitors, clinical trial data about their effect on major disease endpoints in patients with hypertension are still not available. The primary alternatives are the use of randomized trials that include surrogate endpoints, such as level of blood pressure or extent of carotid atherosclerosis, and the use of observational studies that include major disease endpoints. Both approaches, their strengths and limitations, are discussed in detail. The possibility of residual confounding limits the strength of inferences that can be drawn from observational studies. Similarly, the possibility of important drug effects, other than those involving the surrogate endpoint, limits the inferences that can be drawn from randomized trials that rely solely on surrogate outcomes as guides to therapy. In the absence of evidence from large clinical trials that include major disease endpoints, treatment decisions and guidelines need to synthesize the best available information from a variety of sources. Consistency of findings across various study designs, outcomes, and populations is critical to the practice of evidence-based medicine and the effort to maximize the health benefits of antihypertensive therapies.     

Integrating epidemiology, psychology, and economics to achieve HPV vaccination targets

Human papillomavirus (HPV) vaccines provide an opportunity to reduce the incidence of cervical cancer. Optimization of cervical cancer prevention programs requires anticipation of the degree to which the public will adhere to vaccination recommendations. To compare vaccination levels driven by public perceptions with levels that are optimal for maximizing the community's overall utility, we develop an epidemiological game-theoretic model of HPV vaccination. The model is parameterized with survey data on actual perceptions regarding cervical cancer, genital warts, and HPV vaccination collected from parents of vaccine-eligible children in the United States. The results suggest that perceptions of survey respondents generate vaccination levels far lower than those that maximize overall health-related utility for the population. Vaccination goals may be achieved by addressing concerns about vaccine risk, particularly those related to sexual activity among adolescent vaccine recipients. In addition, cost subsidizations and shifts in federal coverage plans may compensate for perceived and real costs of HPV vaccination to achieve public health vaccination targets.     

Contribution of clinical epidemiology to evidence-based nephrology

Improvement of modern medicine does not make day-to-day practice of nephrology easier. Decision-making remains difficult, often requiring numerical, probabilistic approach. Contribution of clinical epidemiology to evidence-based nephrology is not limited to randomised-controlled trial. The concept of natural history: spontaneous evolution without treatment, has been developed in epidemiology to describe diagnosis and prognosis of diseases. For example, in IgA nephropathy, it allowed to measure the prognostic significance of gross hematuria or to design classification of end-stage renal failure. Epidemiologic studies needs appropriate designs, representative samples and correct methods of analysis. Like other medical research, epidemiology has some important methodological limits that must be taking into account. Practising evidence-based nephrology improves confidence in management decisions.     

The epidemiology and economics of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a preventable and treatable disease responsible for a large human and economic burden around the world. Cigarette smoking is the main risk factor for COPD in the developed world, although other important risk factors include occupational exposures, air pollution, airway hyperresponsiveness, asthma, and genetic predisposition. In most of the world, COPD prevalence and mortality continue to rise in response to increases in smoking, particularly by women and adolescents. COPD is also an important cause of disability, and is linked to comorbid diseases, such as depression and cardiovascular disease, which adds to the large economic burden associated with this disorder. Better public health and medical interventions that target both the risk factors for COPD and look toward earlier intervention may decrease the growing public health impact of COPD.     

Chagas cardiomyopathy and heart failure: From epidemiology to treatment

Chagas disease is among the neglected tropical diseases recognized by the World Health Organization that have received insufficient attention from governments and health agencies.Chagas disease is endemic in 21 Latin America regions. Due to globalization and increased migration, it has crossed borders and reached other regions including North America and Europe. The clinical presentation of the disease is highly variable, from general symptoms to severe cardiac involvement that can culminate in heart failure. Chagas heart disease is multifactorial, and can include dilated cardiomyopathy, thromboembolic phenomena, and arrhythmias that may lead to sudden death. Diagnosis is by methods such as enzyme-linked immunosorbent assay (ELISA) and the degree of cardiac involvement should be investigated with complementary exams including ECG, chest radiography and electrophysiological study. There have been insufficient studies on which to base specific treatment for heart failure due to Chagas disease. Treatment should therefore be derived from guidelines for heart failure that are not specific for this disease. Heart transplantation is a viable option with satisfactory success rates that has improved survival.     

从科学证据到临床实践

从1992年加拿大McMaster大学循证医学工作组第一次提出“循证医学”这一名词和概念至今只有短短十五年时间，循证医学已经在世界各地蓬勃发展，其原理内容的阐述以及方法学的探索一直在日新月异的完善和发展，它对医疗实践和卫生决策产生了积极重大的影响，得到了卫生工作者的普遍认同。     

International clinical epidemiology network

The Canadian/American regional group of the International Clinical Epidemiology Network (INCLEN) invites SGIM members to join in an international network dedicated to improving health in low and middle-income countries and reducing health disparities in North America-not only because many goals and activities of the 2 organizations are compatible such as evidence-based medicine, mentoring, and training; but because collaboration between SGIM and INCLEN could strengthen both groups. With increasing brain drain from the developing world to the North, there is an ever-increasing need for academic contributions from the North to swing the balance toward brain gain for the South. SGIM members have the academic expertise to make an important contribution to global health. Participation and contribution from SGIM members is welcomed at the individual or organizational level. We invite you to explore possible partnership and collaboration.     

Modeling the epidemiology and economics of directly observed therapy in Baltimore.

SETTING: From 1958 to 1978, Baltimore maintained one of the highest pulmonary tuberculosis (TB) rates in the US. But, from 1978 to 1992 its TB rate declined by 64.3% and its ranking for TB fell from second highest among large US cites to twenty-eighth. This TB trend coincided with the implementation of an aggressive directly observed therapy (DOT) program by Baltimore's Health Department. OBJECTIVES: We used modeling to estimate the range of TB cases prevented in Baltimore under DOT. Case estimates equal the difference between the observed number of TB cases in Baltimore versus the expected number if Baltimore's TB trend was replaced by the TB trend for the US (low estimate) or the TB trend for all US cities with over 250,000 residents (high estimate). Economic savings are estimated. RESULTS: Without DOT we estimate there would have been between 1,577 (53.6%) and 2,233 (75.9%) more TB cases in Baltimore, costing $18.8 million to $27.1 million. Cases prevented and expenditures saved increased with increased DOT participation. CONCLUSION: Our model predicts that Baltimore's TB decline accompanying DOT resulted in health care savings equal to twice the city's total TB control budget for this period. These results are most plausibly due to DOT, since it was the only major change in Baltimore's TB control program, and rising TB risk factors-AIDS, injection drug use, poverty-in a city where TB had been epidemic should have triggered a TB increase as in comparable US cities, rather than the observed decline. As national TB rates continue to decline it will be important to identify ways to capture and reinvest these savings to support effective TB control programs.     

Creatinine: From physiology to clinical application

Estimating static kidney function accurately and detecting changes in kidney function in a timely fashion are challenging but critically important tasks. Serum creatinine is the most widely used functional biomarker of the kidney. However, its use is associated with substantial shortcomings. Understanding these shortcomings is critical in allowing accurate interpretation of creatinine values and translating them into changes in kidney function. In this review, the pathways involved in creatinine generation and metabolism as well as the techniques involved in measuring creatinine concentrations are discussed. This allows for the discussion of the value and pitfalls in using creatinine as a marker of kidney function. In addition, information regarding alternative functional biomarkers of the kidney is provided.     

Contribution of clinical epidemiology to the management of HIV infections]

INTRODUCTION: The objective of this publication is to make clinicians more aware of clinical epidemiology through our experience in the field of human immunodefiency virus (HIV) infection. CURRENT KNOWLEDGE AND KEY POINTS: Clinical epidemiology is aimed at studying diagnostic, therapeutic and prognostic aspects of diseases, and their consequences, with the objective of improving both scientific knowledge and patient's management. Examples, including longitudinal studies and clinical trials, illustrate the value of clinical epidemiology. FUTURE PROSPECTS AND PROJECTS: Beyond HIV infection, the importance of both multidisciplinary collaboration and education of clinicians in regard to biostatistical and epidemiological methods is emphasized.     

Kinetics methods for clinical epidemiology problems

Calculating the probability of each possible outcome for a patient at any time in the future is currently possible only in the simplest cases: short-term prediction in acute diseases of otherwise healthy persons. This problem is to some extent analogous to predicting the concentrations of species in a reactor when knowing initial concentrations and after examining reaction rates at the individual molecule level. The existing theoretical framework behind predicting contagion and the immediate outcome of acute diseases in previously healthy individuals is largely analogous to deterministic kinetics of chemical systems consisting of one or a few reactions. We show that current statistical models commonly used in chronic disease epidemiology correspond to simple stochastic treatment of single reaction systems. The general problem corresponds to stochastic kinetics of complex reaction systems. We attempt to formulate epidemiologic problems related to chronic diseases in chemical kinetics terms. We review methods that may be adapted for use in epidemiology. We show that some reactions cannot fit into the mass-action law paradigm and solutions to these systems would frequently exhibit an antiportfolio effect. We provide a complete example application of stochastic kinetics modeling for a deductive meta-analysis of two papers on atrial fibrillation incidence, prevalence, and mortality.Much of the medical progress over the last century can be attributed to the objective assessment of the effect of treatments on the evolution of specific diseases. Treatment effect is measured as the rate of an event such as recovery in a sample of the patient population. Relatively immediate results were obtained from studies involving acute diseases, occurring in previously healthy individuals, in which recovery could be clearly identified. This resulted in the development of effective treatments for most acute diseases affecting children and younger adults and a substantial prolongation of life expectancy (1). Consequently, many acute diseases were treated effectively. This led to the current, more complex situation, in which an elderly population suffers from a combination of chronic conditions. Few older people are strictly healthy, and besides the evolution of the chronic conditions themselves, acute diseases occurring in this setting do not always evolve as in a young, healthy population. This combination of chronic conditions and risk factors amounts to the presence of more heterogenous populations. Thus, samples need to be larger to allow reproducible predictions, compared with those for acute diseases occurring in a young and previously healthy population. Predictions that are also more complex (there is no strict “recovery”) apply to a limited range of cases.The concepts used by clinicians and epidemiologists to describe the health status of individuals and their prevalence in the population, as well as the rates of change in this status and the general predictive laws, are quite analogous to the concepts used by chemists for predicting the future concentrations of species in a reactor. Early works on the spread of epidemics of communicable diseases (2, 3) made reference to this analogy, unlike later developments in mathematical epidemiology (4, 5). The purpose of current mathematical modeling of epidemiology is mostly the kinetics (or “dynamics” as it is frequently called) of the spread of a communicable disease in an acute epidemic (6), an important and pressing problem when such epidemics occur. The primary phenomenon represented in these models is contagion. The models used are typically deterministic, self-catalytic kinetic models of the whole population, with mass-action law assumption.The focus of clinical studies in chronic diseases is the risk for various possible outcomes for the patient that are usually distinct from a complete recovery and sometimes of a quantitative nature—for example, how much of the function of an organ is preserved. Kinetics-type mathematical support for this purpose is rarely available, other than a simple statement of risk or relative risk that is directly inferred from a study in a sample.Deterministic event rates are the basis of virtually all clinical judgement. A typical example is, What is the yearly risk of stroke in patients with atrial fibrillation on either of two treatments, such as warfarin or aspirin? (More individual parameters are usually taken into account when classifying each patient). A lower yearly risk rate in one of the treatment categories is an argument to choose that treatment for a particular patient. This risk rate is usually the rate of stroke that has been directly measured in a study where a sample of patients belonging to certain classes (for example, middle-aged males with atrial fibrillation and no history of stroke) has been followed for some time. The observed event rates are taken as the best estimations for the population sharing the same characteristics as the patients in the sample, a population that is presumed infinite. The event rates are inferred, however, starting from observations made in finite, small, ensembles of individuals (case series). Thus, inference is always probabilistic, as event rates in the population can be estimated only with some uncertainty, even in homogenous populations.In populations in which individuals have various combinations of underlying pathologies that may each influence the future event rates, this approach may frequently lead to unreproducible results (7, 8). Aiming to predict events that would occur in the more distant future, as needed with chronic disease, further complicates the problem: The longer the prediction time is, the higher the number of other events that may intervene and invalidate the prediction.Both epidemiology and chemical kinetics have evolved independently over the previous decades, each developing its own stochastic methods with a specific terminology that frequently refers to somewhat similar concepts. Prediction of event rates from relatively small samples, using probabilistic models, is of primary importance for epidemiology. Half a century ago, the factors influencing the recovery from acute diseases in otherwise healthy (that is, homogenous) populations were the main concern. Thus, the problem was to estimate event rates in otherwise simple systems. Models of the epidemiologic equivalent of a single reaction, with a few other parameters, were adequate for this. Probabilistic issues were mostly related to the errors associated with the limited sizes of the samples used, but the inferred rates were typically deterministic. In chemistry, at that time, model development focused on identifying the relatively complex reaction mechanisms that occur, even when only a few initial species are involved, and on describing their kinetics, typically with systems of deterministic differential equations adjusted using macroscopic measurements of species concentrations. Uncertainty due to small molecule numbers was not usually involved. Over the last 70 y, however, chemical kinetics developed new methods, such as models of more complex systems that do not rely on mass-action law (9) or models of single-molecule kinetics that might be closer to the problem of predicting clinical evolution in individual patients. Also, issues that occur in the biochemical kinetics of more complex systems, such as crowding (10), are to some extent analogous to event prediction in heterogenous populations.The development of numerical methods allows the practical approach to problems that involve systems that are both complex and stochastic and the exploration of uncertain phenomena at both individual and population levels (11). An important clinical problem that cannot, in general, be solved without such a systematic approach is to compute, for an individual, the risks for each possible disease over the next time interval (such as 1 y), given what we know about his or her health status and history and based on currently available epidemiologic data. Solving this problem would allow much more accurate planning of clinical interventions than is possible today.In this paper, we attempt to compare concepts, methods, and models that have been developed in the two fields, by reformulating the epidemiologic approaches in chemical kinetics terms, to identify chemical kinetics methods that might be adaptable for epidemiologic use. In Supporting Information, we show an example of a deductive meta-analysis of two epidemiology papers, using a stochastic kinetic system.     

Obesity: epidemiology and clinical aspects

At the beginning of the 21st Century, obesity has become the leading metabolic disease in the World. So much so, that the World Health Organisation refers to obesity as the global epidemic. In fact, obesity is a common disease affecting not only affluent societies but also developing countries. Currently 300 million people can be considered as obese and, due to the rising trend in obesity prevalence, this figure could double by year 2025 if no action is taken against this threat. In terms of health impairment, the importance of obesity lies in the fact that, besides being a disease in itself, it is a risk for many other diseases, mainly from the metabolic and cardiovascular area. Among these, type 2 diabetes, dyslipemia, hyperuricemia, arterial hypertension and cardiovascular disease are the most frequent. Also, respiratory diseases such as obesity hypoventilation syndrome and obstructive sleep apnoea syndrome are strongly associated with obesity.